System and methods for image-guided thermal treatment of tissue

ABSTRACT

A system and methods for modeling the death of tissue cells that are thermally treated using thermal treatment devices is disclosed. A cell-death model accurately predicts, in real-time, which voxels of cells are dead or are about to die as the thermal treatment is applied to these cells. The effects of thermal treatment are monitored by a thermal measurement device which feeds thermal information to the cell-death model. The cell-death model accounts for the temperature of each voxel of tissue cells with respect to a temperature threshold value and the duration over which the thermal treatment is applied. When the thermal measurement device is an imaging device, the results of the thermal treatment may be displayed to the user in real-time. As a result, a user of the thermal treatment device can determine, in real-time, which target voxels of cells he has killed and which still need to be killed. The user can also more easily avoid inadvertently killing healthy tissue that he does not intend to kill. The cell-death model may be implemented in software on the thermal measurement device, on the thermal treatment device, or on a separate processing device which interfaces to and communicates with at least one of the thermal measurement device and the thermal treatment device.

GOVERNMENT SUPPORT

The U.S. Government has a paid-up license in this invention and the right in limited circumstances to require the patent owner to license others on reasonable terms as provided for by the terms of grant no. R01-CA84433 awarded by the National Institute of Health (NIH).

TECHNICAL FIELD

Certain embodiments of the present invention relate to thermal treatment. More particularly, certain embodiments of the present invention relate to a system and methods for image-guided thermal treatment using a cell-death model which indicates the accumulated destruction of voxels of tissue cells over time.

BACKGROUND OF THE INVENTION

An alternative to surgically removing unwanted biological tissue (e.g., malignant tumors, infected tissue, etc.) from a patient is to use thermal treatment to destroy the unwanted tissue. Such thermal treatment is a process of heating up or cooling down (i.e., thermally ablating) biological tissue in order to destroy the cells of the tissue. Additional information with respect to thermal ablation can be found in U.S. Pat. No. 6,542,767 issued to McNichols et al. on Apr. 1, 2003 which is incorporated by reference herein in its entirety.

Interventional image-guided ablation therapies using thermal energy sources such as radio frequency (RF), microwave, laser, high-intensity focused ultrasound (HIFU), and cryogenics have received much recent attention as minimally invasive strategies to cure cancer. Additional information with respect to image-guided ablation therapies using thermal energy sources can be found in U.S. Pat. No. 6,542,767 issued to McNichols et al. on Apr. 1, 2003 which is incorporated by reference herein in its entirety. Potential benefits of such techniques include the ability for near real-time image guidance using magnetic resonance (MR), x-ray computed tomography (CT), or ultrasound, the ability to ablate cancerous tumor in non-surgical candidates, and the potential to perform the procedure on an out-patient basis. Further, it is believed that image-guided ablation therapy may eventually compete with some open tumor surgeries. The successful application of interventional technology to minimally invasive treatment of localized pathologies has the potential to prolong life while reducing the morbidity and cost associated with a more invasive surgical approach.

Typically, a medical doctor (i.e., the user of a thermal treatment tool) will perform the thermal treatment and then later have a medical image of the treated physiological region created to evaluate the success of the treatment. Such a post-treatment evaluation does not allow the doctor to determine, in real-time, if he has successfully destroyed all of the unwanted biological tissue, or if he has inadvertently damaged healthy tissue. Also, post-ablation structural images may not distinguish the edema that surrounds the thermal lesion from tumors, hemorrhage, or prior inflammation.

Methods of image-guided thermal treatment have been developed which use imaging thermometry techniques in conjunction with a cell-death model to allow a user to monitor the effects of the thermal treatment in real or near-real time. However, these cell-death models, used to date, are typically not very robust. The cell-death models often under-estimate or over-estimate the effects of the thermal treatment. Also, a cell-death model which is adequate for treating one type of tissue may not be effective in treating another type of tissue. Additional information with respect to medical thermometry can be found in U.S. Pat. No. 6,542,767 issued to McNichols et al. on Apr. 1, 2003 which is incorporated by reference herein in its entirety.

Some models assume that the tissue response is independent of temperature history. Others use a mathematical model of the temperature-time relationship for tissue damage. Such models usually consist of either a generalized Arrhenius function or a linear approximation of the Arrhenius function near 43° C. However, these models and parameters are based on experiments at lower temperatures and significantly longer heating durations than typical for clinical ablations. Previous studies have shown that the empirically-derived parameters used for these Arrhenius-based models can vary for different tissues, temperature ranges, and heating durations.

A time-temperature product model was not successful, and a critical temperature model which did not account for heating duration did not work very well and was sensitive to transient noise in the temperature data.

Therefore, it is desirable to develop and implement a robust cell-death model which accurately predicts the effects of thermal treatment on many different types of biological tissue.

Further limitations and disadvantages of conventional, traditional, and proposed approaches will become apparent to one of skill in the art, through comparison of such systems and methods with the present invention as set forth in the remainder of the present application with reference to the drawings.

BRIEF SUMMARY OF THE INVENTION

An embodiment of the present invention comprises a method for determining the destruction of at least one voxel of tissue cells in a biological entity as the voxel of tissue cells is thermally treated over time. The method includes measuring at least two temperature values of a voxel of tissue cells, using a thermal measurement device. Each of the temperature values are measured at different times. The method further includes comparing each temperature value to a predefined temperature threshold value to form at least two temperature deviation values, using a processing device. The method also includes generating one rate coefficient value from each of the temperature deviation values, using the processing device. The method further includes generating an accumulated destruction value by at least integrating the rate coefficient values over time, using the processing device. The method also includes comparing the accumulated destruction value to a predetermined destruction threshold value to determine if the voxel of tissue cells is dead or dying, using the processing device.

Another embodiment of the present invention comprises a method of computing an accumulated destruction value used for determining the destruction of a voxel of tissue cells in a biological entity as at least the voxel of tissue cells is thermally treated and as temperature values of the voxel of tissue cells are measured over time. The method includes comparing each of the measured temperature values to a predefined temperature threshold value to form one temperature deviation value for each measured temperature value, using a processing device. The method further includes generating one partial rate coefficient value for each temperature deviation value by mathematically raising each temperature deviation value to a predetermined exponent parameter value, using the processing device. The method also includes generating one full rate coefficient value for each partial rate coefficient value by multiplying each partial rate coefficient value by a predetermined scaling parameter value, using the processing device. The method further includes mathematically integrating the full rate coefficient values over time to form an integrated value, using the processing device. The method also includes mathematically applying an exponential function to a negative of the integrated value to form a partial accumulated destruction value, using the processing device. The method further includes subtracting the partial accumulated destruction value from a value of one to form a normalized accumulated destruction value, using the processing device.

A further embodiment of the present invention comprises a system for treating at least one voxel of tissue cells in a biological entity. The system includes a thermal treatment device for thermally treating at least one voxel of tissue cells. The system further includes a thermal measurement device for measuring temperature values of the voxel of tissue cells over time as the thermal treatment device thermally treats the voxel of tissue cells. The method also includes a processing device for receiving the measured temperature values from the thermal measurement device and for implementing a mathematical cell death model to calculate an accumulated destruction value for the voxel of tissue cells by at least integrating rate coefficient values over time. Each of the rate coefficient values is a mathematical function of a difference between one of the temperature values and a predefined temperature threshold value.

Another embodiment of the present invention comprises a method of pre-planning a thermal treatment of target tissue cells of a biological entity. The method includes implementing a physiological thermal model on a processing device. The physiological thermal model predicts how at least target voxels of tissue cells to be treated in a physiological region of the biological entity will change in temperature over time when thermally treated by any one of a number of possible thermal treatment devices. The method further includes implementing a cell death model on the processing device. The cell death model predicts when any voxel, of at least the target voxels of tissue cells to be treated, is about to die as the voxel of tissue cells is thermally treated. The method also includes using the models on the processing device to perform at least two simulated trial runs of treating the target voxels of tissue cells using at least one of the possible thermal treatment devices according to at least two possible predefined scenarios. The method further includes selecting one of the at least two predefined scenarios as an optimum scenario based on results from the at least two simulated trial runs.

These and other advantages and novel features of the present invention, as well as details of illustrated embodiments thereof, will be more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an exemplary embodiment of a system for thermally treating voxels of tissue cells in a biological entity, in accordance with various aspects of the present invention.

FIG. 2 is a flow chart of an embodiment of a method for determining the destruction of voxels of tissue cells in a biological entity, using the system of FIG. 1, as the voxels of tissue cells are thermally treated over time, in accordance with various aspects of the present invention.

FIG. 3 is a flow chart of an embodiment of a method of computing an accumulated destruction value used for determining the destruction of a voxel of tissue cells in a biological entity, using the system of FIG. 1, as the voxel of tissue cells is thermally treated and as temperature values of the voxel of tissue cells are measured over time, in accordance with various aspects of the present invention.

FIG. 4 is a table of an embodiment of cell death parameter values, used in accordance with various aspects of the present invention.

FIG. 5 illustrates a graphical representation of a plot of the cell death model, in accordance with an embodiment of the present invention.

FIG. 6 is a flow chart of an embodiment of a method of pre-planning a thermal treatment of target tissue cells of a biological entity, in accordance with various aspects of the present invention.

FIG. 7 is a schematic block diagram of an exemplary embodiment of a system for pre-planning a thermal treatment of target tissue cells of a biological entity using the method of FIG. 6, in accordance with various aspects of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an exemplary embodiment of a system 100 for thermally treating voxels of tissue cells in a biological entity, in accordance with various aspects of the present invention. The system includes a thermal treatment device 110, a thermal measurement device 120, a processing device 130, and a display device 150. The processing device 130 includes a cell-death model 140 which may be implemented in software on the processing device 130 (e.g., as a software module).

The thermal treatment device 110 may employ any one of a number of tissue thermal treatment technologies embodied in, for example, a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device. The thermal treatment device 110 is used to thermally treat, either invasively or non-invasively, the voxels of tissue cells to be treated. In the case of, for example, a laser device, the thermal treatment device 110 is used to apply heat to the voxels of tissue cells to be treated. However, in the case of a cryogenic device, the thermal treatment device 110 is used to remove heat from (i.e., cool) the voxels of tissue cells to be treated. As a result, the thermal treatment device 110 is used to destroy (i.e., kill) the voxels of tissue cells through heating or cooling. For example, the voxels of tissue cells may comprise a cancerous tumor in a human patient. Depending on the circumstances (location of tumor in patient, patient's age, etc.), it may be more appropriate to thermally treat the tumor instead of surgically removing the tumor.

The thermal measurement device 120 may employ any one of a number of thermal imaging technologies embodied in, for example, a magnetic resonance (MR) thermometry device, an ultrasound thermometry device, an infrared thermometry device, and a computerized tomography (CT) thermometry device. For example, MR temperature measurements using the proton resonance frequency (PRF) method are accurate, with standard deviations of 1° C., and except for fat tissue, are independent of tissue type. The thermal measurement device may instead include other technologies which may be non-imaging technologies.

Before treatment, a physician can obtain high resolution medical images to plan the thermal treatment procedure. The images will clearly show the pathologic region and the surrounding normal region including critical structures that should be avoided. The physician can display the images and use a cursor, for example, to segment (i.e., mark) the region (i.e., voxels) of pathology with a colored overlay. During the thermal treatment, the model predicted regions of cell death could be overlaid on the same images in a different color. Model predictions can then be visually compared to the segmented pathology to give real-time feedback to the physician. Alternatively, a computer can quantitatively compare the segmented and model predicted voxels of cell death.

As the thermal treatment device 110 is used to heat or cool the voxels of tissue cells to be treated, the thermal measurement device 120 is used to measure the temperature of the voxels of tissue cells over time. The thermal measurements are communicated to the processing device 130 and are used by the cell-death model 140, operating on the processing device 130, to determine when each voxel of the voxels of tissue cells being treated is dead or about to die. In accordance with an embodiment of the present invention, the voxel death information is communicated back to the thermal measurement device 120 (e.g., a magnetic resonance thermometry system) and displayed to a user on the display device 150. The voxel death information may be displayed as, for example, colored voxels of imaged tissue on the display device 150, where a certain color (e.g., red) is used to represent dead or dying voxels of tissue cells. The display device 150 interfaces to the thermal measurement device 120. Other display schemes are possible as well, in accordance with various embodiments of the present invention.

In accordance with an embodiment of the present invention, the processing device 130 comprises a personal computer (PC) or a workstation. The thermal measurement device 120 and the processing device 130 may communicate with each other via hard-wired means (e.g., a network connection) or wirelessly (e.g., a radio frequency (RF) link). In accordance with an alternative embodiment of the present invention, the processing device 130 may be an integral part of the thermal measurement device 120. For example, the thermal measurement device 120 may be the processing device 130, implementing the cell-death model 140. In accordance with a further alternative embodiment of the present invention, the processing device 130 may be an integral part of the thermal treatment device 110 in which case the thermal measurement device would communicate directly with the thermal treatment device 110 via wired or wireless means.

As an option, the processing device 130 may interface to (either wired or wirelessly) the thermal treatment device 110 in order to adapt a control parameter of the thermal treatment device 110. For example, when the processing device 130 determines, using the cell-death model 140, that a pre-designated region of voxels of tissue cells has been killed, the processing device 130 may send a control signal to the thermal treatment device 110 which inhibits the thermal treatment device 110 from producing heat energy (i.e., the thermal treatment device is turned off). Such an automatic control feature helps ensure that voxels of tissue cells that are not intended to be destroyed do not become over heated (or over cooled as in the case of using a cryogenic device).

As yet another option, the thermal measurement device 120 may interface to (either wired or wirelessly) the thermal treatment device 110. For example, the processing device 130 may provide cell death information to the thermal measurement device 120. Then the thermal measurement device 120 may process the cell death information and provide a control signal 141 to the thermal treatment device 110. Other interface schemes are possible as well, in accordance with various embodiments of the present invention.

As the temperature of cells increase during thermal treatment, the kinetic energy of the molecules increases. Such thermal agitation of molecules at high kinetic energies disrupts chemical bonds which can lead to various destructive cell processes including disintegration of cell membrane bilayers, denaturation of cellular proteins, deactivation of enzymes, and damage of ion channels, subcellular organelles, nucleoplasm, and DNA to cause cell death. The severity of such destructive events can increase to a threshold value that leads to cell destruction (i.e., cell death). The vulnerability of tissue to thermal damage depends on molecular structure and strength of chemical bonds but not on cell size or shape. Therefore, the susceptibility to heat damage is very similar across different tissue types.

At elevated temperatures, a normal cell will accumulate “destruction” to a point where it will die. The build up of destruction depends both upon temperature (T) and duration through a temperature dependent rate coefficient β[T(t)]. The temperature response of a heated voxel of tissue during thermal treatment will typically show the temperature increase to a maximum, remain at the maximum for some duration, and then decrease to the basal temperature when heating stops. Above a temperature threshold, T_(c), the destruction of tissue can occur. When the temperature falls below T_(c), destruction stops. In addition, since the rate of tissue destruction increases with temperature, β is a monotonically increasing function with respect to temperature.

FIG. 2 is a flow chart of an embodiment of a method 200 for determining the destruction of voxels of tissue cells in a biological entity, using the system 100 of FIG. 1, as the voxels of tissue cells are thermally treated over time, in accordance with various aspects of the present invention. The voxel dimensions are typically determined by the thermal measurement device. For example, typical voxel dimensions for a MR thermometry imaging system may be 0.6 mm×0.6 mm×1.25 mm. A typical size of any cell within a voxel may be about 20 micro-meters.

In step 210, at least two temperature values of a voxel of tissue cells are measured using a thermal measurement device, and wherein each of the at least two temperature values are measured at different times. The time between measured temperature samples is typically determined by the sampling rate of the thermal measurement device (e.g., maybe 10 Hz for a MR thermometry system). In step 220, each temperature value of the at least two temperature values is compared to a predefined temperature threshold value to form at least two temperature deviation values, using a processing device. In step 230, one rate coefficient value is generated from each of the at least two temperature deviation values, using the processing device. In step 240, an accumulated destruction value is generated by at least integrating the rate coefficient values over time, using the processing device. In step 250, the accumulated destruction value is compared to a predetermined destruction threshold value to determine if the voxel of tissue cells is dead or dying, using the processing device.

In accordance with an embodiment of the present invention, the step 230 of generating the rate coefficient values includes mathematically raising each of the temperature deviation values to a predefined exponent parameter value, N, to form partial rate coefficients. Also, the step 230 of generating the rate coefficient values includes multiplying each of the partial rate coefficient values by a predefined scaling parameter value, A.

For thermal treatment via heating, the rate coefficient value β[T(t)] is mathematically defined as: β[T(t)]=0, for T(t)<T_(c), and β[T(t)]=A*(T(t)−T_(c))^(N), for T(t)≧T_(c) and where any calculated β[T(t)] is the rate coefficient value at a temperature T(t) which is measured from a voxel of tissue cells at any time t, T_(c) is the predefined temperature threshold value, N is the predetermined exponent parameter value, and A is the predetermined scaling parameter value. Notice that T(t) must be greater than or equal to T_(c) for β[T(t)] to have a non-zero value. The difference expression (T(t)−T_(c)) defines the temperature deviation value of step 220 for the case of thermal treatment via heating.

Similarly, in the case of thermal treatment via cooling, the rate coefficient value β[T(t)] may be mathematically defined as: β[T(t)]=0, for T(t)>T_(c), and β[T(t)]=A*(T_(c)−T(t))^(N), for T(t)≦T_(c).

Notice that T(t) must be less than or equal to T_(c) for μ[T(t)] to have a non-zero value. The difference expression (T_(c)−T(t)) defines the temperature deviation value of step 220 for the case of thermal treatment via cooling.

In step 240, the rate coefficient values β[T(t)] are integrated over a time interval 0 to τ according to the mathematical expression: ∫β[T(t)]dt where the time interval 0 to τ corresponds to the time interval over which the temperature values T(t) were measured. The integration may be performed by the processing device 130 according to various numerical integration techniques such as, for example, trapezoidal integration.

In accordance with an embodiment of the present invention, the method 200 is performed in real-time or near-real time, providing feedback to a user (e.g., via the display device 150) as the user thermally treats the voxels of tissue cells.

FIG. 3 is a flow chart of an embodiment of a method 300 of computing an accumulated destruction value used for determining the destruction of a voxel of tissue cells in a biological entity, using the system 100 of FIG. 1, as the voxel of tissue cells is thermally treated and as temperature values of the voxel of tissue cells are measured over time, in accordance with various aspects of the present invention. In step 310, each measured temperature value is compared to a predefined temperature threshold value to form one temperature deviation value for each measured temperature value, using a processing device. In step 320, one partial rate coefficient value is generated for each temperature deviation value by mathematically raising each temperature deviation value to a predetermined exponent parameter value, using the processing device. In step 330, one full rate coefficient value is generated for each partial rate coefficient value by multiplying each partial rate coefficient value by a predetermined scaling parameter value, using the processing device.

In step 340, the full rate coefficient values are mathematically integrated over time to form an integrated value, using the processing device. In step 350, an exponential function is mathematically applied to a negative of the integrated value to form a partial accumulated destruction value, using the processing device. In step 360, the partial accumulated destruction value is subtracted from a value of one to form a normalized accumulated destruction value, using the processing device.

Step 310 of the method 300 is similar to step 220 of the method 200 for forming temperature deviation values. Steps 320 and 330 of the method 300 use the predefined parameters N and A, as previously described herein, to generate the full rate coefficient values. Step 340 of the method 300 integrates the rate coefficient values over time as previously describes before as: integrated value=∫β[T(t)]dt where βT(t)] defines the rate coefficient values.

Step 350 of the method 300 takes the negative of the integrated value and applies the exponential function as: exp[−∫β[T(t)]dt] to form a partial accumulated destruction value.

Step 360 of the method 300 forms the final normalized accumulated destruction value as:

Ω(τ)=1−exp[−∫β[T(t)]dt],

where Ω(τ) is the accumulated destruction value due to heating or cooling of the voxel of tissue cells over a time interval 0 to τ, and wherein t is any time between 0 and τ.

In accordance with an embodiment of the present invention, the method 300 is performed in real-time or near-real time, providing feedback to a user as the user thermally treats the voxels of tissue cells. The cell death model, as mathematically represented by the function Ω(τ), is based on the local time-varying temperature of individual voxels.

For each voxel of tissue cells that are thermally treated (i.e., heated or cooled), the normalized accumulated destruction value Ω(τ) is compared to a predetermined destruction threshold value Ω_(c) to determine if the voxel of tissue cells is dead or dying. That is, the severity of the accumulated destruction increases to the threshold value Ω_(c) that leads to cell death. The determination of cell death is a binary decision. If the accumulated destruction value Ω(τ) is equal to or greater than Ω_(c), then the cell death is assumed to have occurred, otherwise not.

The various parameter values A, N, T_(c), and Ω_(c) may be found experimentally by comparing model results to actual tissue response on a voxel-by-voxel basis. The process of comparing model results to actual tissue response includes applying techniques of image registration, image filtering, and iterative parameter estimation. For example, MR temperature maps may be processed, temporal filtering of an MR temperature image sequence may be performed, registration of post-ablation to temperature MR images may be performed, and segmentation of the cell death region may be performed. The model performance may then be evaluated on a voxel-by-voxel basis in terms of true positive response, true negative response, false positive response, and false negative response.

FIG. 4 is a table 400 of an embodiment of cell death parameter values, used in accordance with various aspects of the present invention. The cell death model described herein includes an adjustable weight, W, such that more or less emphasis can be placed on tumor versus normal tissue destruction. The values of W range from 0 to 1 and, by selecting various parameters estimated at different weights, the mode may be tailored to the clinical treatment. For example, for cancer therapy in organs with regenerative properties, such as the liver, the desired model is that which slightly underestimates the necrotic region to maximize destruction of malignant cells with minimal or acceptable damage to surrounding normal tissue. For such a case, a large W is used to emphasize tumor destruction. For benign tumor eradication, a lower W is desirable to achieve a balance between tumor and normal tissue destruction. The table 400 of FIG. 4 shows four sets of parameters using W=0.50, 0.70, 0.90, and 0.99. For example, the estimated values of the cell death parameters 410 (A, N, T_(c), Ω_(c)) for W=0.99 are 0.0065, 1.0042, 51.6, and 0.6726 respectively.

FIG. 5 illustrates a graphical representation 500 of a plot 503 of the cell death model, in accordance with an embodiment of the present invention. The graph 500 plots the accumulated destruction value Ω(τ) 501 versus Σ 502 where Σ=∫β[T(t)]dt, and Ω(τ)=1−exp[−Σ],

Σ 502 is simply the time-integrated rate coefficient. It can be seen from the graph 500 of FIG. 5 that the plot 503 of Ω(τ) 501 versus Σ 502 is a monotonically increasing function that approaches a value of 1.0, 510. Once Ω(Σ) accumulates to the value Ω_(c) 520, cell death occurs.

FIG. 6 is a flow chart of an embodiment of a method 600 of pre-planning a thermal treatment of target tissue cells of a biological entity, in accordance with various aspects of the present invention. In step 610, a physiological thermal model is implemented on a processing device. The physiological thermal model predicts how at least target voxels of tissue cells to be treated in a physiological region of the biological entity will change in temperature over time when thermally treated by any one of a number of possible thermal treatment devices. In step 620, a cell death model is implemented on the processing device. The cell death model predicts when any voxel, of at least the target voxels of tissue cells to be treated, is about to die as the voxel of tissue cells is thermally treated. In step 630, the models are used, on the processing device, to perform at least two simulated trial runs of treating the target voxels of tissue cells using at least one of the possible thermal treatment devices according to at least two possible predefined scenarios. In step 640, one of the at least two predefined scenarios is selected as an optimum scenario based on the results from the at least two simulated trial runs.

The target tissue cells are those cells which are to be destroyed (i.e., killed) via thermal treatment. The target tissue cells may be, for example, cancerous tumor cells. Tissue cells which are adjacent to or in close proximity to the target tissue cells are presumed to be normal, healthy tissue. It is desirable to kill the target tissue cells without killing the adjacent, healthy tissue cells.

FIG. 7 is a schematic block diagram of an exemplary embodiment of a system 700 for pre-planning a thermal treatment of target tissue cells of a biological entity using the method 700 of FIG. 6, in accordance with various aspects of the present invention. The system 700 includes a processing device 710 (e.g., a PC or a workstation) and a display device 740. The processing device 710 includes a physiological thermal model 720 and a cell-death model 730. The models 720 and 730 may be implemented in software on the processing device 710.

The possible thermal treatment devices to be modeled as part of the phsyiological thermal model 720 include a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device.

The cell-death model 730 may again be of the form described previously herein where, Ω(τ)=1−exp[−∫μ[T(t)]dt, and β[T(t)]=0, for T(t)<T_(c), and β[T(t)]=A*(T(t)−T_(c))N, for T(t)≧T_(c), or β[T(t)]=0, for T(t)>T_(c), and β[T(t)]=A*(T_(c)−T(t))^(N), for T(t)≦T_(c).

Other cell-death models are possible as well, in accordance with various embodiments of the present invention.

Using the method 600 and the system 700, a user may determine a best possible scenario for thermal treatment before actually performing the procedure on a real patient. The user may be able to experiment with different points of entry or points of thermal treatment with respect to the voxels of tissue cells to be treated and determine which scenario produces the most favorable results.

For example, the physiological thermal model 720 can take into account thermal effects due to physiology that is adjacent to the target voxels of tissue cells to be treated. Such adjacent physiology may include blood vessels (i.e., veins and arteries), nerves, and blood-perfused tissue. Each of these physiological entities can have an effect on the thermal treatment of the target voxels (e.g., tumor cells). For example, a blood vessel passing near the target voxels may act as a dissipater of heat energy by carrying heat energy away from the target voxels as blood flows through the blood vessel. A critical nerve may be in close proximity to the target voxels to be treated. As a result, it is important to find a treatment scenario which destroys the target voxels without damaging the critical nerve. The system 700 of FIG. 7 using the method 600 of FIG. 6 can allow the user to find the most optimal treatment scenario, or at least a treatment scenario that is good enough.

The results provided by the method 600 may include identifying which voxels of the target voxels of tissue cells are predicted to die according to the simulated trial runs. Also, the results may include identifying which voxels adjacent to the target voxels are predicted to die according to the simulated results. Ideally, the user wants to kill the target voxels and not kill the adjacent voxels, which are presumably healthy tissue.

An optimum scenario may include that scenario which maximizes a number of target voxels of tissue cells predicted to die and minimizes a number of voxels of healthy tissue cells predicted to die which are adjacent to the target voxels of tissue cells, without damaging any near-by critical physiology (e.g., a critical nerve). An optimum scenario may be defined in other ways as well, in accordance with various embodiments of the present invention.

The method 600 and system 700 may also be used for training medical students and physicians, independent of an actual treatment to be performed on a real patient. As a result, students and physicians can learn about the effects of thermal treatment therapy in a more intuitive manner before performing such a treatment on real tissue.

In summary, a system and methods are disclosed for modeling the destruction of tissue cells that are thermally treated using thermal treatment devices. The cell-death model accurately predicts, in real-time, which voxels of cells are dead or are about to die as the thermal treatment is applied to these cells. The effects of thermal treatment are monitored by a thermal measurement device which feeds thermal information to the cell-death model. The cell-death model accounts for the temperature of each voxel of tissue cells with respect to a temperature threshold value and the duration over which the thermal treatment is applied. When the thermal measurement device is an imaging device, the results of the thermal treatment may be displayed to the user in real-time. As a result, a user of the thermal treatment device can determine, in real-time, which target voxels of cells he has killed and which still need to be killed. The user can also more easily avoid inadvertently killing healthy tissue that he does not intend to kill. The cell-death model may be implemented in software on the thermal measurement device, on the thermal treatment device, or on a separate processing device which interfaces to and communicates with at least one of the thermal measurement device and the thermal treatment device. The cell-death model may also be used in conjunction with a physiological thermal model to pre-plan and simulate a thermal treatment before actually performing the procedure on a real patient.

While the invention has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims. 

1. A method for determining the destruction of at least one voxel of tissue cells in a biological entity as said at least one voxel of tissue cells is thermally treated over time, said method comprising: measuring at least two temperature values of a voxel of tissue cells, using a thermal measurement device, and wherein each of said at least two temperature values are measured at different times; comparing each temperature value of said at least two temperature values to a predefined temperature threshold value to form at least two temperature deviation values, using a processing device; generating one rate coefficient value from each of said at least two temperature deviation values, using said processing device; generating an accumulated destruction value by at least integrating said rate coefficient values over time, using said processing device; and comparing said accumulated destruction value to a predetermined destruction threshold value to determine if said voxel of tissue cells is dead or dying, using said processing device.
 2. The method of claim 1 wherein said at least one voxel of tissue cells is thermally treated over time using a tissue thermal treatment device including one of a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device.
 3. The method of claim 1 wherein said thermal measurement device includes one of a magnetic resonance thermometry device, an ultrasound thermometry device, an infrared thermometry device, and a computerized tomography (CT) thermometry device.
 4. The method of claim 1 wherein said step of generating said rate coefficient values includes mathematically raising each of said at least two temperature deviation values to a predefined exponent parameter value to form at least two partial rate coefficient values.
 5. The method of claim 4 wherein said step of generating said rate coefficient values further includes multiplying each of said at least two partial rate coefficient values by a predefined scaling parameter value.
 6. The method of claim 1 wherein said step of generating said accumulated destruction value further includes mathematically applying an exponential function to a negative of said integrated rate coefficient values to form a partial accumulated destruction value.
 7. The method of claim 6 wherein said step of generating said accumulated destruction value further includes subtracting said partial accumulated destruction value from a value of one to form said accumulated destruction value.
 8. The method of claim 1 wherein said processing device includes one of said thermal measurement device, a personal computer, a work station, a thermal treatment device, and any combination thereof.
 9. The method of claim 1 wherein said method is performed in real-time or near-real-time.
 10. The method of claim 2 further comprising adapting at least one control parameter of said tissue thermal treatment device in response to said accumulated destruction value.
 11. A method of computing an accumulated destruction value used for determining the destruction of a voxel of tissue cells in a biological entity as at least said voxel of tissue cells is thermally treated and as temperature values of said voxel of tissue cells are measured over time, said method comprising: comparing each of said measured temperature values to a predefined temperature threshold value to form one temperature deviation value for each said measured temperature value, using a processing device; generating one partial rate coefficient value for each said temperature deviation value by mathematically raising each said temperature deviation value to a predetermined exponent parameter value, using said processing device; generating one full rate coefficient value for each said partial rate coefficient value by multiplying each said partial rate coefficient value by a predetermined scaling parameter value, using said processing device; mathematically integrating said full rate coefficient values over time to form an integrated value, using said processing device; mathematically applying an exponential function to a negative of said integrated value to form a partial accumulated destruction value, using said processing device; and subtracting said partial accumulated destruction value from a value of one to form a normalized accumulated destruction value, using said processing device.
 12. The method of claim 11 wherein said voxel of tissue cells is thermally treated over time using a tissue thermal treatment device including one of a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device.
 13. The method of claim 11 wherein said temperature values of said voxel of tissue cells are measured over time using one of magnetic resonance thermometry, ultrasound thermometry, infrared thermometry, and computerized tomography (CT) thermometry.
 14. The method of claim 11 wherein said normalized accumulated destruction value is computed in real-time or near-real-time as said voxel of tissue cells is being thermally treated and measured for temperature.
 15. The method of claim 11 wherein said processing device comprises one of a thermal imaging device, a personal computer (PC), a work station, a thermal treatment device, and any combination thereof.
 16. The method of claim 11 further comprising comparing said normalized accumulated destruction value to a predetermined destruction threshold value to determine if said voxel of tissue cells is dead or dying, using said processing device.
 17. The method of claim 12 further comprising adapting at least one control parameter of said tissue thermal treatment device in response to said normalized accumulated destruction value.
 18. A system for treating at least one voxel of tissue cells in a biological entity, said system comprising: a thermal treatment device for thermally treating at least one voxel of tissue cells; a thermal measurement device for measuring temperature values of said voxel of tissue cells over time as said thermal treatment device thermally treats said voxel of tissue cells; and a processing device for receiving said measured temperature values from said thermal measurement device and for implementing a mathematical cell death model to calculate an accumulated destruction value for said voxel of tissue cells by at least integrating rate coefficient values over time, wherein each of said rate coefficient values is a mathematical function of a difference between one of said measured temperature values and a predefined temperature threshold value.
 19. The system of claim 18 wherein each of said rate coefficient values is calculated from a mathematical function of the form: β[T(t)]=0, for T(t)<T_(c), and β[T(t)]=A*(T(t)−T_(c))^(N), for T(t)≧T_(c), and wherein any said calculated β[T(t)] is said rate coefficient value at a temperature T(t) which is measured from said voxel of tissue cells at any time t, T_(c) is said predefined temperature threshold value, N is a predetermined exponent parameter value, and A is a predetermined scaling parameter value.
 20. The system of claim 19 wherein said accumulated destruction value is calculated from a function of the form: Ω(τ)=1−exp[−∫β[T(t)]dt], where Ω(τ) is said accumulated destruction value due to thermal heating of said voxel of tissue cells over a time interval 0 to τ, and wherein t is any time between 0 and τ.
 21. The system of claim 18 wherein each of said rate coefficient values is calculated from a mathematical function of the form: β[T(t)]=0, for T(t)>T_(c), and β[T(t)]=A*(T_(c)−T(t))^(N), for T(t)≦T_(c), and wherein any said calculated β[T(t)] is said rate coefficient value at a temperature T(t) which is measured from said voxel of tissue cells at any time t, T_(c) is said predefined temperature threshold value, N is a predetermined exponent parameter value, and A is a predetermined scaling parameter value.
 22. The system of claim 21 wherein said accumulated destruction value is calculated from a function of the form: Ω(τ)=1−exp[−∫β[T(t)]dt], where Ω(τ) is said accumulated destruction value due to thermal cooling of said voxel of tissue cells over a time interval 0 to τ, and wherein t is any time between 0 and τ.
 23. The system of claim 18 wherein said thermal treatment device comprises one of a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device.
 24. The system of claim 18 wherein said thermal measurement device comprises one of a magnetic resonance thermometry device, an ultrasound thermometry device, an infrared thermometry device, and a computerized tomography (CT) thermometry device.
 25. The system of claim 18 wherein said processing device includes one of said thermal measurement device, a personal computer (PC), a work station, said thermal treatment device, and any combination thereof.
 26. The system of claim 18 further comprising at least one display monitor connected to at least one of said processing device, said thermal measurement device, and said thermal treatment device to display real-time or near-real-time information to a user of said system.
 27. The system of claim 26 wherein said information indicates to said user whether or not said voxel of tissue cells is dead or is dying.
 28. The system of claim 18 wherein said voxel of tissue cells is declared to be dead or dying when said accumulated destruction value is equal to or greater than a predefined destruction threshold value.
 29. The system of claim 18 wherein said voxel of tissue cells is declared to be dead or dying when said accumulated destruction value is equal to or less than a predefined destruction threshold value.
 30. The system of claim 18 wherein at least one control parameter of said thermal treatment device is adapted in response to said accumulated destruction value.
 31. A method of pre-planning a thermal treatment of target tissue cells of a biological entity, said method comprising: implementing a physiological thermal model on a processing device, wherein said physiological thermal model predicts how at least target voxels of tissue cells to be treated in a physiological region of said biological entity will change in temperature over time when thermally treated by any one of a number of possible thermal treatment devices; implementing a cell death model on said processing device, wherein said cell death model predicts when any voxel, of at least said target voxels of tissue cells to be treated, is about to die as said any voxel of tissue cells is thermally treated; using said models on said processing device to perform at least two simulated trial runs of treating said target voxels of tissue cells using at least one of said possible thermal treatment devices according to at least two possible predefined scenarios; and selecting one of said at least two predefined scenarios as an optimum scenario based on results from said at least two simulated trial runs.
 32. The method of claim 31 wherein said possible thermal treatment devices to be modeled as part of said physiological thermal model include a laser device, an ultrasound device, a radio frequency (RF) device, a microwave device, a resistive heater device, and a cryogenic device.
 33. The method of claim 31 wherein said cell death model includes a mathematical function of the form: Ω(τ)=1−exp[−∫β[T(t)]dt], wherein Ω(τ) is an accumulated destruction value corresponding to said any voxel of tissue cells after thermally treating said any voxel of tissue cells over a time interval 0 to τ, and wherein β[T(t)] defines a mathematical function for computing a thermal rate coefficient value for said any voxel of tissue cells as a function of temperature T(t) of said any voxel of tissue cells at any time t between 0 and τ.
 34. The method of claim 33 wherein said function β[T(t)] is of the form: β[T(t)]=0, for T(t)<T_(c), and β[T(t)]=A*(T(t)−T_(c))^(N), for T(t)≧T_(c), and wherein T(t) is a predicted temperature of said any voxel of tissue cells at said any time t from said physiological thermal model, T_(c) is a predefined temperature threshold value, N is a predetermined exponent parameter value, and A is a predetermined scaling parameter value.
 35. The method of claim 33 wherein said function β[T(t)] is of the form: β[T(t)]=0, for T(t)>T_(c), and β[T(t)]=A*(T_(c)−T(t))^(N), for T(t)≦T_(c), and wherein T(t) is a predicted temperature of said any voxel of tissue cells at said any time t from said physiological thermal model, T_(c) is a predefined temperature threshold value, N is a predetermined exponent parameter value, and A is a predetermined scaling parameter value.
 36. The method of claim 31 wherein said physiological thermal model takes into account thermal effects due to physiology adjacent to said target voxels of tissue cells including blood vessels, nerves, and blood-profuse tissue.
 37. The method of claim 31 wherein said results include identifying which voxels of said target voxels of tissue cells are predicted to die according to said simulated trial runs.
 38. The method of claim 31 wherein said results include identifying which voxels adjacent to said target voxels are predicted to die according to said simulated trial runs.
 39. The method of claim 31 wherein said optimum scenario includes that scenario which maximizes a number of said target voxels of tissue cells predicted to die and minimizes a number of voxels of tissue cells predicted to die which are adjacent to said target voxels of tissue cells. 